Final Assignment

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library(tidyverse)
library(dplyr)
library(forcats)
library(gmodels)
library(haven)

###Read data

#Put the complete name of the countries. 
Disc <- Disc |> mutate(country = case_when(
  isocntry== "BE" ~ "Belgium",
  isocntry== "DK" ~ "Denmark",
  isocntry== "GR" ~ "Greece",
  isocntry== "ES" ~ "Spain",
  isocntry== "FI" ~"Finland",
  isocntry== "FR" ~ "France",
  isocntry== "IE" ~ "Ireland",
  isocntry== "IT" ~ "Italy",
  isocntry== "LU" ~ "Luxembourg",
  isocntry== "NL" ~ "Netherlands",
  isocntry== "AT" ~ "Austria",
  isocntry== "PT" ~ "Portugal",
  isocntry== "SE" ~ "Sweden",
  isocntry== "DE-W" ~ "Germany (specifically the state of North Rhine-Westphalia)",
  isocntry== "DE-E" ~ "Germany (specifically the state of Berlin)",
  isocntry== "GB" ~ "United Kingdom",
  isocntry== "BG" ~ "Bulgaria",
  isocntry== "CY" ~ "Cyprus",
  isocntry== "CZ" ~ "Czech Republic",
  isocntry== "EE" ~ "Estonia",
  isocntry== "HU" ~ "Hungary",
  isocntry== "LV" ~ "Latvia",
  isocntry== "LT" ~ "Lithuania",
  isocntry== "MT" ~ "Malta",
  isocntry== "PL" ~ "Poland",
  isocntry== "RO" ~ "Romania",
  isocntry== "SK" ~ "Slovakia",
  isocntry== "SI" ~ "Slovenia",
  isocntry== "HR" ~ "Croatia",
  TRUE ~ NA_character_))

view(Disc)

Convert numeric answers from qc19 to character answers

Disc <- Disc %>% mutate(change_docs = case_when(
  qc19 == 1 ~ "Yes",
  qc19 == 2 ~ "No", 
  qc19 == 3 ~ "DK", 
  TRUE ~ NA_character_)) 

Count the qc19 answers by country

library(dplyr)

#sum counts 
opinions_by_country <- Disc %>%
  group_by(country, change_docs) %>%
  summarise(count = n()) %>%
  pivot_wider(names_from = change_docs, values_from = count, values_fill = 0)

opinions_by_country

#percentage counts 
Disc %>%
  group_by(country, change_docs) %>%
  summarise(count = n()) %>%
  ungroup() %>%
  group_by(country) %>%
  mutate(total_count = sum(count)) %>%
  mutate(percentage = (count / total_count) * 100) %>%
  select(country, change_docs, percentage) %>%
  pivot_wider(names_from = change_docs, values_from = percentage, values_fill = 0)

#Top 5 of countries that think that transgender persons should be able to change their civil documents to match their inner gender identity
country_with_most_yes <- opinions_by_country %>%
  filter(Yes > 0) %>%
  arrange(desc(Yes)) %>%
  head(5)
country_with_most_yes

#Top 5 of countries that do not think that transgender persons should be able to change their civil documents to match their inner gender identity
country_with_most_no <- opinions_by_country %>%
  filter(No > 0) %>%
  arrange(desc(No)) %>%
  head(5)
country_with_most_no

#Top 5 of countries that do not think that transgender persons should be able to change their civil documents to match their inner gender identity
country_with_most_DK <- opinions_by_country %>%
  filter(DK > 0) %>%
  arrange(desc(DK)) %>%
  head(5)
country_with_most_DK